73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
import torch
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import numpy as np
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from PIL import Image
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import os
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class LoadImageByPathNode:
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"""
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按路径加载图像的节点
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image_path": ("STRING", {
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"default": "E:\\ComfyUIModels\\input\\10\\1.png",
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"multiline": False
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "load_image_by_path"
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CATEGORY = "Rui-Node🐶/文件存储与加载📁"
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def load_image_by_path(self, image_path):
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"""
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根据指定路径加载图像
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参数:
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image_path: 图像文件的完整路径
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返回:
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加载的图像张量
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"""
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try:
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# 检查文件是否存在
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if not os.path.exists(image_path):
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raise FileNotFoundError(f"图像文件不存在: {image_path}")
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# 使用 PIL 加载图像
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img_pil = Image.open(image_path)
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# 确保图像为 RGB 模式
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if img_pil.mode != 'RGB':
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img_pil = img_pil.convert('RGB')
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# 将 PIL 图像转换为 NumPy 数组
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img_np = np.array(img_pil).astype(np.float32) / 255.0
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# 转换为 PyTorch 张量并添加批次维度
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# ComfyUI 期望的格式为 BHWC
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img_tensor = torch.from_numpy(img_np).unsqueeze(0)
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return (img_tensor,)
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except Exception as e:
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# 如果加载失败，创建一个默认的黑色图像
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print(f"加载图像失败: {str(e)}")
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# 创建一个 512x512 的黑色图像作为默认值
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default_img = np.zeros((512, 512, 3), dtype=np.float32)
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default_tensor = torch.from_numpy(default_img).unsqueeze(0)
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return (default_tensor,)
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# 节点映射字典，用于 ComfyUI 注册节点
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NODE_CLASS_MAPPINGS = {
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"LoadImageByPath": LoadImageByPathNode
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}
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# 节点显示名称映射，用于在 UI 中显示友好名称
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LoadImageByPath": "按路径加载图像 / Load Image By Path"
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} |